
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt
import seaborn as sns 
import plotly.express as px 


pd.set_option('display.max_column', None)
pd.set_option('display.max_rows', None)
pd.set_option('display.max_seq_items', None)
pd.set_option('display.max_colwidth', 500)
pd.set_option('expand_frame_repr', True)


# load datadet. source: Word Bank Website (https://data.worldbank.org/country/NG)
df=pd.read_csv('API_NGA_DS2_en_csv_v2_5455596.csv')


df.head()


# Lets make a copy of the original dataset 
df_copy =df.copy()


# Lets view the first five rows of our dataset
df_copy.head()


# Lets drop the first 3 rows and create a copy of the dataset  
df_copy2=df_copy.drop([0,1,2], axis =0).copy()


# Check the result after dropping first 3 rows 
df_copy2.head()


# reset the index 
df_copy2.reset_index()


# Covert row 3 to column headers 
df_copy2.columns=df_copy2.iloc[0]


# Checking result 
df_copy2.head()


# drop redundant columns and save in new variable 
df_copy3= df_copy2.drop(['Country Name','Country Code','Indicator Code'], axis=1)


# Check the first five rows of the result 
df_copy3.head()


df_copy3.set_index('Indicator Name').T.copy()


df_copy3.head()


# Drop row with index number 3 from the dataset since it has been used has colum headers
df_copy4=df_copy3.drop([3], axis =0).copy()


df_copy4.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1478 entries, 4 to 1481
Data columns (total 64 columns):
 #   Column          Non-Null Count  Dtype  
---  ------          --------------  -----  
 0   Indicator Name  1478 non-null   object 
 1   1960.0          170 non-null    float64
 2   1961.0          195 non-null    float64
 3   1962.0          215 non-null    float64
 4   1963.0          232 non-null    float64
 5   1964.0          234 non-null    float64
 6   1965.0          239 non-null    float64
 7   1966.0          235 non-null    float64
 8   1967.0          238 non-null    float64
 9   1968.0          243 non-null    float64
 10  1969.0          244 non-null    float64
 11  1970.0          341 non-null    float64
 12  1971.0          357 non-null    float64
 13  1972.0          363 non-null    float64
 14  1973.0          356 non-null    float64
 15  1974.0          340 non-null    float64
 16  1975.0          345 non-null    float64
 17  1976.0          350 non-null    float64
 18  1977.0          422 non-null    float64
 19  1978.0          418 non-null    float64
 20  1979.0          415 non-null    float64
 21  1980.0          400 non-null    float64
 22  1981.0          527 non-null    float64
 23  1982.0          538 non-null    float64
 24  1983.0          550 non-null    float64
 25  1984.0          544 non-null    float64
 26  1985.0          559 non-null    float64
 27  1986.0          560 non-null    float64
 28  1987.0          541 non-null    float64
 29  1988.0          552 non-null    float64
 30  1989.0          569 non-null    float64
 31  1990.0          697 non-null    float64
 32  1991.0          685 non-null    float64
 33  1992.0          702 non-null    float64
 34  1993.0          678 non-null    float64
 35  1994.0          674 non-null    float64
 36  1995.0          707 non-null    float64
 37  1996.0          759 non-null    float64
 38  1997.0          702 non-null    float64
 39  1998.0          737 non-null    float64
 40  1999.0          766 non-null    float64
 41  2000.0          878 non-null    float64
 42  2001.0          796 non-null    float64
 43  2002.0          832 non-null    float64
 44  2003.0          919 non-null    float64
 45  2004.0          846 non-null    float64
 46  2005.0          919 non-null    float64
 47  2006.0          954 non-null    float64
 48  2007.0          940 non-null    float64
 49  2008.0          964 non-null    float64
 50  2009.0          917 non-null    float64
 51  2010.0          1028 non-null   float64
 52  2011.0          992 non-null    float64
 53  2012.0          928 non-null    float64
 54  2013.0          992 non-null    float64
 55  2014.0          989 non-null    float64
 56  2015.0          999 non-null    float64
 57  2016.0          981 non-null    float64
 58  2017.0          920 non-null    float64
 59  2018.0          1027 non-null   float64
 60  2019.0          919 non-null    float64
 61  2020.0          836 non-null    float64
 62  2021.0          656 non-null    float64
 63  2022.0          54 non-null     float64
dtypes: float64(63), object(1)
memory usage: 739.1+ KB


# Select only column header titled indicator name
df_indicator_name=df_copy4['Indicator Name']


# Slect all date columns without indicator name 
df_date_series = df_copy4.iloc[:,1:]


df_indicator_name.reindex().head()

4                                                    Intentional homicides (per 100,000 people)
5    Internally displaced persons, new displacement associated with disasters (number of cases)
6             Voice and Accountability: Percentile Rank, Upper Bound of 90% Confidence Interval
7                                                            Voice and Accountability: Estimate
8                                                         High-technology exports (current US$)
Name: Indicator Name, dtype: object


df_indicator_name=pd.DataFrame(df_indicator_name)


# This is to create a new dataframe for ease of transposition with new index  
df_combined=pd.concat([df_indicator_name, df_date_series], ignore_index=True)


df_combined.head()


df_list = df_combined['Indicator Name'].drop_duplicates().to_list()


len(df_list)

1479


date_indexed =pd.DataFrame(df_date_series.T).reset_index()


date_indexed.head()


# Create a copy of the year column and insert in the dataset   
date_indexed['year']=date_indexed[3]


# Rename the column headers with indicator values but leaving our 
df_cols=date_indexed.drop([3], axis=1)


df_cols.head()


df_cols.columns=df_list


# This is to replace the Column header as Year instead of NaN
df_cols.columns =df_cols.columns.fillna("Year")


df_cols.head()


# Change the year column from float data to integer 
df_cols['Year']=df_cols['Year'].astype('int', copy=True)


df_cols.head()


# Save the nw dataframe 
df_new = df_cols


df_new = df_new.round(1)


df_new.index = df_new['Year']


# Check percentage of missing values and output as a dataframe 
check_missing_values =pd.DataFrame((df_new.isnull().sum()/df_new.shape[0])*100).round(2).sort_values(by= 0, ascending=False)
check_missing_values=check_missing_values.set_axis(['Missing Values%'], axis=1)
check_missing_values.columns.rename('Indicator Name', inplace=True)
check_missing_values.head()


# create a loop to delete multiple columns with missing values 
# lets use a tight threshold of 10% to capture more data 
df_dropped_cols = df_new
for i in df_dropped_cols.columns:
    if (df_dropped_cols[i].isnull().sum()/df_dropped_cols.shape[0])*100 >5.0:
        del df_dropped_cols[i]


# Lets check the result 
check_missing_values =pd.DataFrame((df_dropped_cols.isnull().sum()/df_dropped_cols.shape[0])*100).round(2).sort_values(by= 0, ascending=False)
check_missing_values=check_missing_values.set_axis(['Missing Values%'], axis=1)
check_missing_values.columns.rename('Indicator Name', inplace=True)
check_missing_values.head()


# Lets check the shape 
df_dropped_cols.shape

(63, 186)


# Lets fill the missing values with zeros  
df_filled=df_dropped_cols.fillna(0).copy()


# Check if the result after dealing with missing values 
df_filled.isnull().sum().head()

Merchandise exports to low- and middle-income economies within region (% of total merchandise exports)              0
Merchandise imports from low- and middle-income economies in Sub-Saharan Africa (% of total merchandise imports)    0
Merchandise imports (current US$)                                                                                   0
Urban population                                                                                                    0
Rural population                                                                                                    0
dtype: int64


df_filled.describe().round(1).T


plt.figure(figsize=(15,10))
ax=sns.barplot(data=df_filled,  y= df_filled['Net migration'], x=df_filled['Year'])
ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha="right")
plt.tight_layout()
plt.show()


plt.figure(figsize=(15,10))
ax=sns.barplot(data=df_new,  y= df_filled['GDP growth (annual %)'], x=df_filled['Year'])
ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha="right")
plt.tight_layout()
plt.show()


df_new['GDP growth (annual %)'].mean().round(2)


df_new['GDP growth (annual %)'].max()


# Find The Year when 
df_new['GDP growth (annual %)'].loc[df_new['GDP growth (annual %)']==25]


df_new['GDP growth (annual %)'].min()


# This is to find the last non null or zero value and return the previous
x=len(df_filled['GDP growth (annual %)'])
    
for i in reversed(range(x)):
    if df_filled['GDP growth (annual %)'].iloc[i]==0.0:
          if df_filled['GDP growth (annual %)'].iloc[(i-1)]!=0:
            print (df_filled[['GDP growth (annual %)','Year']].iloc[(i-1)] )


plt.figure(figsize=(15,10))
ax=sns.barplot(data=df_new,  y= df_filled['Trade (% of GDP)'], x=df_filled['Year'])
ax.set_xticklabels(ax.get_xticklabels(), rotation=90, ha="right")
plt.tight_layout()
plt.show()


# Create a dataframe selecting only 21 years of Data 

df_10y = df_filled.query("Year >= 2000 and Year <2022")[['Inflation, consumer prices (annual %)', 'GDP growth (annual %)']]


# Check correlations 
df_10y.corr()


# Plot data 
px.area(data_frame=df_10y)

Indicator Name	Missing Values%
Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)	100.0
Survey mean consumption or income per capita, bottom 40% of population (2017 PPP $ per day)	100.0
Customs and other import duties (current LCU)	100.0
Taxes on exports (% of tax revenue)	100.0
Social contributions (% of revenue)	100.0

Indicator Name	Missing Values%
Merchandise exports to low- and middle-income economies within region (% of total merchandise exports)	4.76
Renewable internal freshwater resources, total (billion cubic meters)	4.76
Arable land (hectares)	4.76
Merchandise exports by the reporting economy, residual (% of total merchandise exports)	4.76
Merchandise exports to high-income economies (% of total merchandise exports)	4.76

	count	mean	std	min	25%	50%	75%	max
Merchandise exports to low- and middle-income economies within region (% of total merchandise exports)	63.0	5.700000e+00	3.900000e+00	0.000000e+00	1.900000e+00	6.500000e+00	8.400000e+00	1.320000e+01
Merchandise imports from low- and middle-income economies in Sub-Saharan Africa (% of total merchandise imports)	63.0	2.400000e+00	2.100000e+00	0.000000e+00	6.000000e-01	1.200000e+00	4.200000e+00	8.500000e+00
Merchandise imports (current US$)	63.0	1.712511e+10	1.833237e+10	5.400000e+08	3.973000e+09	8.986000e+09	2.889800e+10	5.830000e+10
Urban population	63.0	3.785171e+07	3.074279e+07	0.000000e+00	1.213700e+07	2.825959e+07	5.627249e+07	1.125607e+08
Rural population	63.0	6.766952e+07	2.146527e+07	0.000000e+00	4.986762e+07	6.695467e+07	8.613775e+07	1.008407e+08
Population, male	63.0	5.304648e+07	2.589949e+07	0.000000e+00	3.101947e+07	4.782165e+07	7.164094e+07	1.078270e+08
Age dependency ratio, old (% of working-age population)	63.0	5.900000e+00	8.000000e-01	0.000000e+00	5.800000e+00	6.000000e+00	6.100000e+00	6.200000e+00
Population ages 80 and above, female (% of female population)	63.0	4.000000e-01	1.000000e-01	0.000000e+00	3.000000e-01	4.000000e-01	4.000000e-01	4.000000e-01
Population ages 70-74, female (% of female population)	63.0	1.000000e+00	1.000000e-01	0.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	1.100000e+00
Population ages 65 and above, male	63.0	1.550912e+06	7.326447e+05	0.000000e+00	9.215325e+05	1.431572e+06	2.073338e+06	3.045923e+06
Population ages 65-69, female (% of female population)	63.0	1.500000e+00	2.000000e-01	0.000000e+00	1.400000e+00	1.500000e+00	1.500000e+00	1.700000e+00
Population ages 55-59, female (% of female population)	63.0	2.500000e+00	4.000000e-01	0.000000e+00	2.300000e+00	2.500000e+00	2.700000e+00	2.800000e+00
Population ages 45-49, female (% of female population)	63.0	3.700000e+00	5.000000e-01	0.000000e+00	3.500000e+00	3.700000e+00	3.900000e+00	4.000000e+00
Population ages 35-39, female (% of female population)	63.0	5.200000e+00	7.000000e-01	0.000000e+00	5.100000e+00	5.400000e+00	5.500000e+00	5.600000e+00
Population ages 25-29, female (% of female population)	63.0	7.200000e+00	1.000000e+00	0.000000e+00	7.100000e+00	7.300000e+00	7.600000e+00	7.800000e+00
Population ages 15-64, total	63.0	5.604120e+07	2.693588e+07	0.000000e+00	3.338156e+07	4.955228e+07	7.562504e+07	1.146655e+08
Population ages 15-64, female	63.0	2.790941e+07	1.333382e+07	0.000000e+00	1.668272e+07	2.472128e+07	3.768740e+07	5.673876e+07
Population ages 10-14, female (% of female population)	63.0	1.160000e+01	1.600000e+00	0.000000e+00	1.140000e+01	1.180000e+01	1.220000e+01	1.260000e+01
Population ages 0-14, total	63.0	4.617739e+07	2.274906e+07	0.000000e+00	2.661492e+07	4.261084e+07	6.238935e+07	9.237297e+07
Population ages 0-14, female	63.0	2.281361e+07	1.117344e+07	0.000000e+00	1.321583e+07	2.105178e+07	3.075938e+07	4.551866e+07
Survival to age 65, male (% of cohort)	63.0	3.540000e+01	7.200000e+00	0.000000e+00	3.370000e+01	3.600000e+01	3.970000e+01	4.510000e+01
Life expectancy at birth, total (years)	63.0	4.520000e+01	7.400000e+00	0.000000e+00	4.320000e+01	4.610000e+01	4.950000e+01	5.290000e+01
Birth rate, crude (per 1,000 people)	63.0	4.340000e+01	6.300000e+00	0.000000e+00	4.280000e+01	4.380000e+01	4.650000e+01	4.820000e+01
Domestic credit to private sector (% of GDP)	63.0	8.400000e+00	3.500000e+00	0.000000e+00	6.200000e+00	7.600000e+00	1.050000e+01	1.960000e+01
Broad money growth (annual %)	63.0	2.240000e+01	1.980000e+01	-1.270000e+01	9.200000e+00	1.600000e+01	3.280000e+01	8.920000e+01
Claims on private sector (annual growth as % of broad money)	63.0	1.220000e+01	1.240000e+01	-1.060000e+01	4.900000e+00	1.120000e+01	1.600000e+01	6.510000e+01
Aquaculture production (metric tons)	63.0	6.858990e+04	1.062155e+05	0.000000e+00	4.745000e+03	1.503000e+04	7.046650e+04	3.167270e+05
Population in urban agglomerations of more than 1 million	63.0	1.460358e+07	1.022793e+07	2.145512e+06	5.386776e+06	1.266302e+07	2.204222e+07	3.730653e+07
DEC alternative conversion factor (LCU per US$)	63.0	7.550000e+01	1.014000e+02	0.000000e+00	8.000000e-01	9.400000e+00	1.284000e+02	3.994000e+02
GNI per capita (current LCU)	63.0	1.303490e+05	2.170695e+05	0.000000e+00	3.330000e+02	4.917400e+03	1.792038e+05	7.933639e+05
GNI (current LCU)	63.0	2.343986e+13	4.270405e+13	0.000000e+00	2.066935e+10	4.682110e+11	2.557250e+13	1.693050e+14
GDP per capita growth (annual %)	63.0	1.000000e+00	6.800000e+00	-1.750000e+01	-2.000000e+00	1.400000e+00	3.600000e+00	2.230000e+01
GDP growth (annual %)	63.0	3.600000e+00	6.900000e+00	-1.570000e+01	0.000000e+00	4.200000e+00	6.600000e+00	2.500000e+01
GDP (current US$)	63.0	1.391305e+11	1.675377e+11	0.000000e+00	2.629942e+10	5.445784e+10	2.070630e+11	5.741840e+11
Inflation, GDP deflator (annual %)	63.0	1.740000e+01	2.960000e+01	-3.100000e+00	5.400000e+00	1.010000e+01	1.980000e+01	2.190000e+02
Inflation, consumer prices (annual %)	63.0	1.560000e+01	1.520000e+01	-3.700000e+00	7.400000e+00	1.170000e+01	1.680000e+01	7.280000e+01
Broad money (% of GDP)	63.0	1.560000e+01	6.200000e+00	0.000000e+00	1.120000e+01	1.300000e+01	2.190000e+01	2.860000e+01
Net foreign assets (current LCU)	63.0	2.201232e+12	3.496468e+12	-4.361610e+09	6.108872e+08	8.584316e+09	4.594945e+12	1.183840e+13
Total reserves minus gold (current US$)	63.0	1.209176e+10	1.631132e+10	0.000000e+00	6.049532e+08	4.075717e+09	2.209430e+10	5.300058e+10
Renewable internal freshwater resources, total (billion cubic meters)	63.0	2.105000e+02	4.740000e+01	0.000000e+00	2.210000e+02	2.210000e+02	2.210000e+02	2.210000e+02
Total fisheries production (metric tons)	63.0	4.403226e+05	3.328574e+05	0.000000e+00	2.530460e+05	2.795140e+05	5.975220e+05	1.212475e+06
Population in largest city	63.0	5.956931e+06	4.379390e+06	7.624180e+05	1.949966e+06	5.067003e+06	9.343337e+06	1.538764e+07
Net bilateral aid flows from DAC donors, Germany (current US$)	63.0	7.354270e+07	2.603535e+08	0.000000e+00	4.565000e+06	1.332000e+07	2.607500e+07	1.710410e+09
Net bilateral aid flows from DAC donors, Canada (current US$)	63.0	1.154889e+07	1.503315e+07	0.000000e+00	1.145000e+06	3.300000e+06	1.658000e+07	6.039000e+07
Technical cooperation grants (BoP, current US$)	63.0	1.137695e+08	1.110731e+08	0.000000e+00	3.741000e+07	6.359000e+07	2.096850e+08	3.765900e+08
Food production index (2014-2016 = 100)	63.0	5.070000e+01	3.170000e+01	0.000000e+00	2.320000e+01	3.990000e+01	7.890000e+01	1.117000e+02
Permanent cropland (% of land area)	63.0	4.900000e+00	1.900000e+00	0.000000e+00	3.500000e+00	4.200000e+00	7.000000e+00	7.400000e+00
Arable land (hectares)	63.0	2.834900e+07	8.437574e+06	0.000000e+00	2.600000e+07	3.010000e+07	3.500000e+07	3.700000e+07
Merchandise exports by the reporting economy, residual (% of total merchandise exports)	63.0	4.600000e+00	5.800000e+00	0.000000e+00	1.000000e+00	1.200000e+00	7.400000e+00	2.280000e+01
Merchandise exports to high-income economies (% of total merchandise exports)	63.0	7.120000e+01	2.330000e+01	0.000000e+00	6.140000e+01	8.080000e+01	8.840000e+01	9.310000e+01
Merchandise imports by the reporting economy (current US$)	63.0	1.356884e+10	1.543975e+10	0.000000e+00	1.685600e+09	7.010515e+09	1.862919e+10	5.150229e+10
Merchandise imports from low- and middle-income economies outside region (% of total merchandise imports)	63.0	1.260000e+01	9.400000e+00	0.000000e+00	5.100000e+00	9.500000e+00	1.900000e+01	3.590000e+01
Rural population (% of total population)	63.0	6.830000e+01	1.440000e+01	0.000000e+00	5.960000e+01	6.980000e+01	8.000000e+01	8.460000e+01
Population, female	63.0	5.247474e+07	2.526649e+07	0.000000e+00	3.098515e+07	4.739261e+07	7.076930e+07	1.055743e+08
Population growth (annual %)	63.0	2.500000e+00	5.000000e-01	0.000000e+00	2.400000e+00	2.600000e+00	2.700000e+00	3.100000e+00
Sex ratio at birth (male births per female births)	63.0	1.000000e+00	1.000000e-01	0.000000e+00	1.000000e+00	1.000000e+00	1.000000e+00	1.100000e+00
Population ages 75-79, female (% of female population)	63.0	6.000000e-01	1.000000e-01	0.000000e+00	5.000000e-01	6.000000e-01	6.000000e-01	6.000000e-01
Population ages 65 and above, total	63.0	3.302634e+06	1.500911e+06	0.000000e+00	2.008139e+06	3.051131e+06	4.395857e+06	6.362813e+06
Population ages 65 and above, female	63.0	1.751722e+06	7.684351e+05	0.000000e+00	1.086607e+06	1.619559e+06	2.322518e+06	3.316890e+06
Population ages 60-64, female (% of female population)	63.0	2.000000e+00	3.000000e-01	0.000000e+00	1.900000e+00	2.000000e+00	2.100000e+00	2.300000e+00
Population ages 50-54, female (% of female population)	63.0	3.000000e+00	4.000000e-01	0.000000e+00	2.900000e+00	3.100000e+00	3.300000e+00	3.300000e+00
Population ages 40-44, female (% of female population)	63.0	4.400000e+00	6.000000e-01	0.000000e+00	4.200000e+00	4.500000e+00	4.600000e+00	4.800000e+00
Population ages 30-34, female (% of female population)	63.0	6.200000e+00	8.000000e-01	0.000000e+00	6.000000e+00	6.300000e+00	6.500000e+00	6.600000e+00
Population ages 20-24, female (% of female population)	63.0	8.500000e+00	1.100000e+00	0.000000e+00	8.300000e+00	8.500000e+00	8.800000e+00	9.300000e+00
Population ages 15-64, male	63.0	2.813179e+07	1.360237e+07	0.000000e+00	1.669884e+07	2.483101e+07	3.793764e+07	5.792678e+07
Population ages 15-19, female (% of female population)	63.0	9.900000e+00	1.300000e+00	0.000000e+00	9.700000e+00	1.000000e+01	1.040000e+01	1.080000e+01
Population ages 05-09, female (% of female population)	63.0	1.380000e+01	1.800000e+00	0.000000e+00	1.350000e+01	1.390000e+01	1.450000e+01	1.480000e+01
Population ages 0-14, male	63.0	2.336378e+07	1.157566e+07	0.000000e+00	1.339910e+07	2.155907e+07	3.162996e+07	4.685431e+07
Population ages 00-04, female (% of female population)	63.0	1.720000e+01	2.300000e+00	0.000000e+00	1.720000e+01	1.730000e+01	1.770000e+01	1.850000e+01
Fertility rate, total (births per woman)	63.0	6.200000e+00	9.000000e-01	0.000000e+00	6.100000e+00	6.400000e+00	6.500000e+00	6.900000e+00
Mortality rate, adult, female (per 1,000 female adults)	63.0	3.790000e+02	5.600000e+01	0.000000e+00	3.654000e+02	3.715000e+02	3.910000e+02	4.468000e+02
Official exchange rate (LCU per US$, period average)	63.0	6.420000e+01	9.350000e+01	0.000000e+00	7.000000e-01	7.400000e+00	1.272000e+02	3.588000e+02
GDP per capita (constant LCU)	63.0	2.601495e+05	6.891870e+04	0.000000e+00	2.068407e+05	2.397249e+05	3.139641e+05	3.792516e+05
GDP per capita (current US$)	63.0	9.712000e+02	8.855000e+02	0.000000e+00	2.954000e+02	5.653000e+02	1.860600e+03	3.201000e+03
GDP (constant LCU)	63.0	2.975073e+13	2.033797e+13	0.000000e+00	1.703145e+13	2.181560e+13	3.995185e+13	7.338280e+13
GDP (current LCU)	63.0	2.441616e+13	4.432264e+13	0.000000e+00	2.127470e+10	4.946440e+11	2.674855e+13	1.760760e+14
External balance on goods and services (% of GDP)	63.0	2.900000e+00	6.900000e+00	-7.700000e+00	-2.800000e+00	2.300000e+00	7.400000e+00	2.310000e+01
Imports of goods and services (% of GDP)	63.0	1.410000e+01	5.400000e+00	0.000000e+00	1.160000e+01	1.370000e+01	1.740000e+01	2.390000e+01
Imports of goods and services (current LCU)	63.0	3.455127e+12	6.279413e+12	0.000000e+00	3.940493e+09	4.921688e+10	3.512870e+12	2.884080e+13
Exports of goods and services (current US$)	63.0	2.540325e+10	3.390491e+10	0.000000e+00	3.730466e+09	1.111111e+10	3.601886e+10	1.463670e+11
Net primary income (Net income from abroad) (current LCU)	63.0	-9.762968e+11	1.679718e+12	-6.770590e+12	-1.400310e+12	-2.643250e+10	-5.938500e+08	0.000000e+00
GNI per capita, Atlas method (current US$)	63.0	9.087000e+02	8.306000e+02	0.000000e+00	3.100000e+02	5.600000e+02	1.395000e+03	2.970000e+03
GNI (current US$)	63.0	1.335784e+11	1.609826e+11	0.000000e+00	2.469406e+10	5.177758e+10	1.983375e+11	5.550230e+11
GDP per capita (constant 2015 US$)	63.0	1.838100e+03	4.869000e+02	0.000000e+00	1.461400e+03	1.693700e+03	2.218300e+03	2.679600e+03
GDP (constant 2015 US$)	63.0	2.102000e+11	1.436954e+11	0.000000e+00	1.203335e+11	1.541350e+11	2.822745e+11	5.184770e+11
Claims on central government, etc. (% GDP)	63.0	3.700000e+00	5.000000e+00	-7.500000e+00	6.000000e-01	3.300000e+00	7.400000e+00	1.630000e+01
Consumer price index (2010 = 100)	63.0	4.850000e+01	8.220000e+01	0.000000e+00	2.000000e-01	2.400000e+00	6.390000e+01	3.543000e+02
Broad money (current LCU)	63.0	5.676011e+12	1.102599e+13	0.000000e+00	3.313650e+09	5.755363e+10	3.087795e+12	4.381850e+13
Net domestic credit (current LCU)	63.0	5.291319e+12	1.105927e+13	-3.140000e+08	1.267450e+09	6.166478e+10	1.623335e+12	4.852140e+13
Domestic credit to private sector by banks (% of GDP)	63.0	8.200000e+00	3.500000e+00	0.000000e+00	6.000000e+00	7.500000e+00	1.030000e+01	1.960000e+01
Capture fisheries production (metric tons)	63.0	3.717327e+05	2.345743e+05	0.000000e+00	2.441985e+05	2.662020e+05	5.268010e+05	9.162840e+05
Population in urban agglomerations of more than 1 million (% of total population)	63.0	1.180000e+01	3.700000e+00	4.800000e+00	8.400000e+00	1.300000e+01	1.500000e+01	1.710000e+01
Population density (people per sq. km of land area)	63.0	1.114000e+02	5.720000e+01	0.000000e+00	6.620000e+01	1.018000e+02	1.522000e+02	2.287000e+02
Net bilateral aid flows from DAC donors, Japan (current US$)	63.0	4.270063e+07	2.052784e+08	-1.085000e+07	2.100000e+05	8.670000e+06	2.479000e+07	1.631610e+09
Grants, excluding technical cooperation (BoP, current US$)	63.0	6.274198e+08	1.657714e+09	0.000000e+00	8.095000e+06	3.552000e+07	8.100150e+08	1.138818e+10
Cereal yield (kg per hectare)	63.0	1.144200e+03	3.877000e+02	0.000000e+00	8.494000e+02	1.203000e+03	1.410800e+03	1.733400e+03
Crop production index (2014-2016 = 100)	63.0	5.070000e+01	3.170000e+01	0.000000e+00	2.300000e+01	3.980000e+01	7.860000e+01	1.126000e+02
Land under cereal production (hectares)	63.0	1.343894e+07	4.849629e+06	0.000000e+00	1.047300e+07	1.440000e+07	1.761890e+07	1.941000e+07
Agricultural land (% of land area)	63.0	6.420000e+01	1.610000e+01	0.000000e+00	6.060000e+01	6.760000e+01	7.350000e+01	7.630000e+01
Merchandise exports to low- and middle-income economies in Sub-Saharan Africa (% of total merchandise exports)	63.0	5.700000e+00	3.900000e+00	0.000000e+00	1.900000e+00	6.500000e+00	8.400000e+00	1.320000e+01
Merchandise exports (current US$)	63.0	2.649876e+10	3.039863e+10	4.620000e+08	7.120000e+09	1.234200e+10	4.154950e+10	1.160000e+11
Merchandise imports by the reporting economy, residual (% of total merchandise imports)	63.0	8.800000e+00	6.500000e+00	0.000000e+00	5.000000e+00	7.000000e+00	1.500000e+01	2.280000e+01
Merchandise imports from high-income economies (% of total merchandise imports)	63.0	7.140000e+01	2.160000e+01	0.000000e+00	5.860000e+01	8.240000e+01	8.650000e+01	9.000000e+01
Merchandise trade (% of GDP)	63.0	3.330000e+01	1.330000e+01	0.000000e+00	2.360000e+01	3.070000e+01	4.110000e+01	6.640000e+01
Urban population (% of total population)	63.0	3.010000e+01	1.210000e+01	0.000000e+00	1.960000e+01	2.970000e+01	3.950000e+01	5.270000e+01
Rural population growth (annual %)	63.0	1.600000e+00	5.000000e-01	0.000000e+00	1.200000e+00	1.700000e+00	1.800000e+00	2.500000e+00
Population, male (% of total population)	63.0	4.940000e+01	6.300000e+00	0.000000e+00	5.000000e+01	5.020000e+01	5.030000e+01	5.050000e+01
Population, total	63.0	1.055212e+08	5.116578e+07	0.000000e+00	6.200462e+07	9.521426e+07	1.424102e+08	2.134013e+08
Age dependency ratio, young (% of working-age population)	63.0	8.030000e+01	1.090000e+01	0.000000e+00	7.980000e+01	8.230000e+01	8.420000e+01	8.690000e+01
Population ages 80 and above, male (% of male population)	63.0	3.000000e-01	1.000000e-01	0.000000e+00	3.000000e-01	3.000000e-01	3.000000e-01	3.000000e-01
Population ages 70-74, male (% of male population)	63.0	9.000000e-01	1.000000e-01	0.000000e+00	9.000000e-01	9.000000e-01	9.000000e-01	9.000000e-01
Population ages 65 and above, male (% of male population)	63.0	2.900000e+00	4.000000e-01	0.000000e+00	2.900000e+00	3.000000e+00	3.000000e+00	3.000000e+00
Population ages 65-69, male (% of male population)	63.0	1.300000e+00	2.000000e-01	0.000000e+00	1.300000e+00	1.300000e+00	1.400000e+00	1.500000e+00
Population ages 55-59, male (% of male population)	63.0	2.400000e+00	3.000000e-01	0.000000e+00	2.200000e+00	2.400000e+00	2.500000e+00	2.600000e+00
Population ages 45-49, male (% of male population)	63.0	3.600000e+00	5.000000e-01	0.000000e+00	3.400000e+00	3.800000e+00	3.900000e+00	4.000000e+00
Population ages 35-39, male (% of male population)	63.0	5.200000e+00	7.000000e-01	0.000000e+00	5.000000e+00	5.400000e+00	5.600000e+00	5.700000e+00
Population ages 25-29, male (% of male population)	63.0	7.300000e+00	1.000000e+00	0.000000e+00	7.000000e+00	7.300000e+00	7.700000e+00	7.800000e+00
Population ages 15-64 (% of total population)	63.0	5.250000e+01	6.800000e+00	0.000000e+00	5.260000e+01	5.310000e+01	5.370000e+01	5.570000e+01
Population ages 15-64, female (% of female population)	63.0	5.250000e+01	6.800000e+00	0.000000e+00	5.270000e+01	5.320000e+01	5.380000e+01	5.550000e+01
Population ages 10-14, male (% of male population)	63.0	1.170000e+01	1.600000e+00	0.000000e+00	1.140000e+01	1.200000e+01	1.240000e+01	1.270000e+01
Population ages 0-14 (% of total population)	63.0	4.280000e+01	5.600000e+00	0.000000e+00	4.290000e+01	4.370000e+01	4.430000e+01	4.500000e+01
Population ages 0-14, female (% of female population)	63.0	4.250000e+01	5.600000e+00	0.000000e+00	4.270000e+01	4.350000e+01	4.400000e+01	4.470000e+01
Life expectancy at birth, male (years)	63.0	4.430000e+01	7.400000e+00	0.000000e+00	4.250000e+01	4.490000e+01	4.860000e+01	5.270000e+01
Death rate, crude (per 1,000 people)	63.0	1.860000e+01	4.600000e+00	0.000000e+00	1.540000e+01	1.880000e+01	2.090000e+01	2.620000e+01
Adolescent fertility rate (births per 1,000 women ages 15-19)	63.0	1.396000e+02	3.320000e+01	0.000000e+00	1.279000e+02	1.408000e+02	1.628000e+02	1.854000e+02
Net official development assistance received (constant 2020 US$)	63.0	1.121659e+09	1.901569e+09	0.000000e+00	2.255850e+08	3.704300e+08	1.517115e+09	1.215208e+10
Merchandise exports by the reporting economy (current US$)	63.0	2.479199e+10	3.056652e+10	0.000000e+00	2.820300e+09	1.182250e+10	3.760875e+10	1.129340e+11
Merchandise exports to low- and middle-income economies outside region (% of total merchandise exports)	63.0	1.380000e+01	1.640000e+01	0.000000e+00	1.300000e+00	3.300000e+00	2.440000e+01	4.970000e+01
Merchandise imports from low- and middle-income economies within region (% of total merchandise imports)	63.0	2.400000e+00	2.100000e+00	0.000000e+00	6.000000e-01	1.200000e+00	4.200000e+00	8.500000e+00
Merchandise imports from low- and middle-income economies in South Asia (% of total merchandise imports)	63.0	2.300000e+00	1.300000e+00	0.000000e+00	1.200000e+00	2.500000e+00	3.400000e+00	5.100000e+00
Urban population growth (annual %)	63.0	4.400000e+00	1.100000e+00	0.000000e+00	4.100000e+00	4.500000e+00	5.000000e+00	6.100000e+00
Population, female (% of total population)	63.0	4.900000e+01	6.300000e+00	0.000000e+00	4.970000e+01	4.980000e+01	5.000000e+01	5.010000e+01
Age dependency ratio (% of working-age population)	63.0	8.620000e+01	1.160000e+01	0.000000e+00	8.580000e+01	8.810000e+01	9.010000e+01	9.310000e+01
Population ages 75-79, male (% of male population)	63.0	5.000000e-01	1.000000e-01	0.000000e+00	5.000000e-01	5.000000e-01	5.000000e-01	5.000000e-01
Population ages 65 and above (% of total population)	63.0	3.100000e+00	4.000000e-01	0.000000e+00	3.100000e+00	3.200000e+00	3.200000e+00	3.400000e+00
Population ages 65 and above, female (% of female population)	63.0	3.400000e+00	5.000000e-01	0.000000e+00	3.300000e+00	3.400000e+00	3.500000e+00	3.700000e+00
Population ages 60-64, male (% of male population)	63.0	1.800000e+00	2.000000e-01	0.000000e+00	1.800000e+00	1.900000e+00	1.900000e+00	2.100000e+00
Population ages 50-54, male (% of male population)	63.0	3.000000e+00	4.000000e-01	0.000000e+00	2.800000e+00	3.100000e+00	3.100000e+00	3.200000e+00
Population ages 40-44, male (% of male population)	63.0	4.400000e+00	6.000000e-01	0.000000e+00	4.200000e+00	4.500000e+00	4.700000e+00	4.800000e+00
Population ages 30-34, male (% of male population)	63.0	6.200000e+00	8.000000e-01	0.000000e+00	6.000000e+00	6.300000e+00	6.600000e+00	6.600000e+00
Population ages 20-24, male (% of male population)	63.0	8.600000e+00	1.200000e+00	0.000000e+00	8.300000e+00	8.700000e+00	9.100000e+00	9.300000e+00
Population ages 15-64, male (% of male population)	63.0	5.240000e+01	6.800000e+00	0.000000e+00	5.250000e+01	5.300000e+01	5.370000e+01	5.590000e+01
Population ages 15-19, male (% of male population)	63.0	1.000000e+01	1.300000e+00	0.000000e+00	9.800000e+00	1.020000e+01	1.050000e+01	1.090000e+01
Population ages 05-09, male (% of male population)	63.0	1.390000e+01	1.900000e+00	0.000000e+00	1.360000e+01	1.410000e+01	1.460000e+01	1.490000e+01
Population ages 0-14, male (% of male population)	63.0	4.310000e+01	5.600000e+00	0.000000e+00	4.320000e+01	4.410000e+01	4.460000e+01	4.530000e+01
Population ages 00-04, male (% of male population)	63.0	1.750000e+01	2.300000e+00	0.000000e+00	1.740000e+01	1.770000e+01	1.800000e+01	1.880000e+01
Survival to age 65, female (% of cohort)	63.0	3.910000e+01	7.100000e+00	0.000000e+00	3.640000e+01	4.100000e+01	4.330000e+01	4.660000e+01
Life expectancy at birth, female (years)	63.0	4.620000e+01	7.300000e+00	0.000000e+00	4.400000e+01	4.750000e+01	5.040000e+01	5.330000e+01
Mortality rate, adult, male (per 1,000 male adults)	63.0	4.157000e+02	6.440000e+01	0.000000e+00	3.968000e+02	4.158000e+02	4.375000e+02	5.248000e+02
Military expenditure (% of GDP)	63.0	1.900000e+00	2.400000e+00	0.000000e+00	5.000000e-01	8.000000e-01	2.200000e+00	1.030000e+01
Net bilateral aid flows from DAC donors, United Kingdom (current US$)	63.0	1.732859e+08	4.876229e+08	0.000000e+00	7.025000e+06	1.999000e+07	1.574900e+08	3.185740e+09
Livestock production index (2014-2016 = 100)	63.0	5.740000e+01	3.130000e+01	0.000000e+00	2.790000e+01	5.170000e+01	8.690000e+01	1.038000e+02
Average precipitation in depth (mm per year)	63.0	1.095200e+03	2.469000e+02	0.000000e+00	1.150000e+03	1.150000e+03	1.150000e+03	1.150000e+03
Arable land (hectares per person)	63.0	3.000000e-01	1.000000e-01	0.000000e+00	2.000000e-01	3.000000e-01	4.000000e-01	6.000000e-01
Fertilizer consumption (kilograms per hectare of arable land)	63.0	6.900000e+00	5.800000e+00	0.000000e+00	1.000000e+00	5.900000e+00	1.060000e+01	2.100000e+01
Net primary income (Net income from abroad) (current US$)	63.0	-5.552147e+09	7.117880e+09	-2.577629e+10	-9.255934e+09	-2.230850e+09	-5.752432e+08	0.000000e+00
GNI, Atlas method (current US$)	63.0	1.315834e+11	1.614906e+11	0.000000e+00	2.308718e+10	5.438922e+10	1.678195e+11	5.335140e+11
GDP per capita (current LCU)	63.0	1.359191e+05	2.254019e+05	0.000000e+00	3.428000e+02	5.195100e+03	1.875181e+05	8.250910e+05
GDP deflator (base year varies by country)	63.0	4.100000e+01	6.370000e+01	0.000000e+00	1.000000e-01	2.300000e+00	6.680000e+01	2.399000e+02
Trade (% of GDP)	63.0	3.110000e+01	1.200000e+01	0.000000e+00	2.270000e+01	3.130000e+01	4.020000e+01	5.330000e+01
Net bilateral aid flows from DAC donors, Total (current US$)	63.0	6.478684e+08	1.609097e+09	0.000000e+00	4.277000e+07	7.418000e+07	7.497700e+08	1.097039e+10
Surface area (sq. km)	63.0	8.797810e+05	1.983051e+05	0.000000e+00	9.237700e+05	9.237700e+05	9.237700e+05	9.237700e+05
Cereal production (metric tons)	63.0	1.645230e+07	8.686462e+06	0.000000e+00	8.283500e+06	1.800700e+07	2.245880e+07	3.064709e+07
Land area (sq. km)	63.0	8.674000e+05	1.955144e+05	0.000000e+00	9.107700e+05	9.107700e+05	9.107700e+05	9.107700e+05
Arable land (% of land area)	63.0	3.110000e+01	9.300000e+00	0.000000e+00	2.850000e+01	3.300000e+01	3.840000e+01	4.060000e+01
Agricultural land (sq. km)	63.0	5.850229e+05	1.469287e+05	0.000000e+00	5.515000e+05	6.158600e+05	6.691884e+05	6.945005e+05
Imports of goods and services (current US$)	63.0	1.952367e+10	2.489800e+10	0.000000e+00	2.276938e+09	8.116202e+09	2.738661e+10	8.977767e+10
Military expenditure (current LCU)	63.0	1.343627e+11	2.914021e+11	0.000000e+00	8.308000e+08	2.229000e+09	1.040005e+11	1.783120e+12
Net ODA received per capita (current US$)	63.0	6.200000e+00	1.200000e+01	0.000000e+00	8.000000e-01	1.700000e+00	9.500000e+00	7.920000e+01
Net official development assistance and official aid received (constant 2020 US$)	63.0	1.121659e+09	1.901569e+09	0.000000e+00	2.255850e+08	3.704300e+08	1.517115e+09	1.215208e+10
Broad money to total reserves ratio	63.0	2.800000e+00	2.000000e+00	0.000000e+00	1.400000e+00	2.400000e+00	3.800000e+00	9.300000e+00
Monetary Sector credit to private sector (% GDP)	63.0	8.400000e+00	3.500000e+00	0.000000e+00	6.200000e+00	7.600000e+00	1.050000e+01	1.960000e+01
Claims on central government (annual growth as % of broad money)	63.0	5.500000e+00	2.900000e+01	-1.354000e+02	-4.000000e-01	7.000000e+00	1.940000e+01	7.420000e+01
Total reserves (includes gold, current US$)	63.0	1.221278e+10	1.625357e+10	0.000000e+00	7.779215e+08	4.329392e+09	2.224475e+10	5.300058e+10
Renewable internal freshwater resources per capita (cubic meters)	63.0	2.418900e+03	1.231500e+03	0.000000e+00	1.470300e+03	2.206000e+03	3.368400e+03	4.819500e+03
Population in the largest city (% of urban population)	63.0	1.520000e+01	1.500000e+00	1.100000e+01	1.400000e+01	1.540000e+01	1.650000e+01	1.730000e+01
Net ODA received (% of GNI)	63.0	6.000000e-01	8.000000e-01	0.000000e+00	3.000000e-01	5.000000e-01	8.000000e-01	4.900000e+00
External balance on goods and services (current LCU)	63.0	6.014893e+11	2.538091e+12	-8.129610e+12	-2.328468e+08	2.469100e+09	3.612245e+11	1.342900e+13
Military expenditure (current USD)	63.0	1.182859e+09	1.037019e+09	0.000000e+00	3.044628e+08	8.187845e+08	1.925180e+09	4.466398e+09
Net official development assistance received (current US$)	63.0	9.659622e+08	1.856802e+09	0.000000e+00	5.497000e+07	1.678200e+08	1.466475e+09	1.143196e+10
Net official development assistance and official aid received (current US$)	63.0	9.659622e+08	1.856802e+09	0.000000e+00	5.497000e+07	1.678200e+08	1.466475e+09	1.143196e+10
Net migration	63.0	1.413330e+04	8.909320e+04	-5.075390e+05	-2.761500e+03	2.108000e+04	6.132050e+04	1.844670e+05
External balance on goods and services (current US$)	63.0	5.879572e+09	1.525394e+10	-2.501417e+10	-3.099721e+08	1.366603e+09	5.300368e+09	8.611676e+10
Exports of goods and services (% of GDP)	63.0	1.700000e+01	8.200000e+00	0.000000e+00	1.020000e+01	1.800000e+01	2.410000e+01	3.600000e+01
Exports of goods and services (current LCU)	63.0	4.056619e+12	6.616222e+12	0.000000e+00	4.571950e+09	1.047560e+11	6.113675e+12	2.282440e+13
Year	63.0	1.991000e+03	1.830000e+01	1.960000e+03	1.975500e+03	1.991000e+03	2.006500e+03	2.022000e+03

EXPLORATORY ANALYSIS ON NIGERIA'S WORLD BANK DATASET¶

About Nigeria¶

Some Insights¶

Import Necessary Libraries¶

Import Dataset¶

Lets Explore the Dataset¶

Lets learn about the dataset¶

¶

Data Preprocessing¶

Split Columns¶

Checking for Missing Values¶

What is the Trend of Net Migration in Nigeria since 1960?¶

GDP growth (annual %)¶

What is the Trend of Nigeria's Annual GDP Growth Rate¶

What is the average GDP growth rate since 1960?¶

What is the Highest GDP Growth Rate since 1960¶

What is the Lowest GDP Growth Rate since 1960¶

What was the last GDP Growth Recorded ?¶

Observations:¶

What is the Trend in Nigeria's Trade (% of GDP) since 1960¶

Is there any correlation between GDP growth and Inflation between 2000 and 2021¶

¶

Conclusions¶

	Data Source	World Development Indicators	Unnamed: 2	Unnamed: 3	Unnamed: 4	Unnamed: 5	Unnamed: 6	Unnamed: 7	Unnamed: 8	Unnamed: 9	Unnamed: 10	Unnamed: 11	Unnamed: 12	Unnamed: 13	Unnamed: 14	Unnamed: 15	Unnamed: 16	Unnamed: 17	Unnamed: 18	Unnamed: 19	Unnamed: 20	Unnamed: 21	Unnamed: 22	Unnamed: 23	Unnamed: 24	Unnamed: 25	Unnamed: 26	Unnamed: 27	Unnamed: 28	Unnamed: 29	Unnamed: 30	Unnamed: 31	Unnamed: 32	Unnamed: 33	Unnamed: 34	Unnamed: 35	Unnamed: 36	Unnamed: 37	Unnamed: 38	Unnamed: 39	Unnamed: 40	Unnamed: 41	Unnamed: 42	Unnamed: 43	Unnamed: 44	Unnamed: 45	Unnamed: 46	Unnamed: 47	Unnamed: 48	Unnamed: 49	Unnamed: 50	Unnamed: 51	Unnamed: 52	Unnamed: 53	Unnamed: 54	Unnamed: 55	Unnamed: 56	Unnamed: 57	Unnamed: 58	Unnamed: 59	Unnamed: 60	Unnamed: 61	Unnamed: 62	Unnamed: 63	Unnamed: 64	Unnamed: 65	Unnamed: 66
0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	Last Updated Date	5/10/2023	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	Country Name	Country Code	Indicator Name	Indicator Code	1960.0	1961.0	1962.0	1963.0	1964.0	1965.0	1966.0	1967.0	1968.0	1969.0	1970.0	1971.0	1972.0	1973.0	1974.0	1975.0	1976.0	1977.0	1978.0	1979.0	1980.0	1981.0	1982.0	1983.0	1984.0	1985.0	1986.0	1987.0	1988.0	1989.0	1990.0	1991.0	1992.0	1993.0	1994.0	1995.0	1996.0	1997.0	1998.0	1999.0	2000.0	2001.0	2002.0	2003.0	2004.0	2005.0	2006.0	2007.0	2008.0	2009.0	2010.0	2011.0	2012.0	2013.0	2014.0	2015.0	2016.000000	2017.0	2018.0	2019.000000	2020.0	2021.0	2022.0
4	Nigeria	NGA	Intentional homicides (per 100,000 people)	VC.IHR.PSRC.P5	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	33.604193	NaN	NaN	21.740789	NaN	NaN	NaN

	Indicator Name	1960.0	1961.0	1962.0	1963.0	1964.0	1965.0	1966.0	1967.0	1968.0	1969.0	1970.0	1971.0	1972.0	1973.0	1974.0	1975.0	1976.0	1977.0	1978.0	1979.0	1980.0	1981.0	1982.0	1983.0	1984.0	1985.0	1986.0	1987.0	1988.0	1989.0	1990.0	1991.0	1992.0	1993.0	1994.0	1995.0	1996.0	1997.0	1998.0	1999.0	2000.0	2001.0	2002.0	2003.0	2004.0	2005.0	2006.0	2007.0	2008.0	2009.0	2010.0	2011.0	2012.0	2013.0	2014.0	2015.0	2016.0	2017.0	2018.0	2019.0	2020.0	2021.0	2022.0
0	Intentional homicides (per 100,000 people)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	Internally displaced persons, new displacement associated with disasters (number of cases)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	Voice and Accountability: Percentile Rank, Upper Bound of 90% Confidence Interval	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	Voice and Accountability: Estimate	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	High-technology exports (current US$)	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	Inflation, consumer prices (annual %)	GDP growth (annual %)
Inflation, consumer prices (annual %)	1.000000	-0.108584
GDP growth (annual %)	-0.108584	1.000000